AI Metadata Enrichment Pipeline for Data Quality and Coverage
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Solution Overview
Problem
Traditional data enrichment techniques face challenges such as high costs, resource-intensiveness, and inefficiencies due to reliance on third-party metadata aggregation and web-scraped open-source datasets, which often contain poor-quality information, while internally-sourced data may lack breadth and depth.
Innovation Solution
An AI model-based pipeline is used to generate and validate sample metadata, reducing dependency on external sources by leveraging advanced AI techniques for cost-effective and comprehensive data enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data enrichment techniques using third-party metadata aggregation and web-scraped datasets are used, then data coverage is improved, but data quality deteriorates due to poor-quality information from external sources
Solution Approach 1:
The patent introduces an AI model as an intermediary between external data sources and the final enriched dataset. The AI model processes and validates metadata from third-party sources, filtering out poor-quality information while preserving useful data coverage. This intermediary layer resolves the contradiction by enabling broad data collection while maintaining high quality standards through intelligent validation.
Solution Approach 2:
The system employs internally-sourced data and AI-driven validation to reduce dependency on external third-party sources. By leveraging internal resources and automated AI validation, the system serves its own data enrichment needs without relying on potentially low-quality external aggregations, thus maintaining data quality while achieving sufficient coverage.
2Quantity of substance
If traditional data enrichment techniques involving multiple external sources are used, then data completeness is improved, but processing complexity and costs increase
Solution Approach 1:
The patent merges multiple data enrichment functions into a single AI model processing step. Instead of separately aggregating metadata from multiple third-party sources, scraping web datasets, and validating each source independently, the AI model consolidates these functions into one unified process that achieves data completeness with reduced complexity.
Solution Approach 2:
The system extracts only the essential validation and enrichment functions from complex external processing pipelines. By taking out the core functionality needed for data completeness and implementing it through a streamlined AI model, the system reduces processing complexity while maintaining data completeness requirements.
3Reliability
If extensive data curation and validation processes are applied, then data quality is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent replaces manual or rule-based data validation mechanisms with an AI model that performs validation automatically. This substitution of mechanical validation processes with intelligent automation maintains high data quality standards while significantly improving processing efficiency and reducing resource consumption.
Solution Approach 2:
The AI model dynamically adjusts validation parameters and processing depth based on the specific characteristics of incoming data. By changing validation parameters adaptively rather than applying fixed extensive validation to all data, the system maintains high data quality while optimizing processing efficiency for different data types and quality levels.
Data Source
AI summary
The present disclosure provides an approach of generating a request to obtain information corresponding to a data sample. The approach produces, by a processing device, sample metadata using an artificial intelligence (AI) model trained to analyze the data sample and generate the sample metadata. In turn, the approach enriches the data sample based on the sample metadata to produce an enriched data sample.